SoroushVahidi/scidocs huggingface dataset:
Vegan diets: practical advice for athletes and exercisers
(2017)

With the growth of social media as a platform to share information, veganism is becoming more visible, and could be becoming more accepted in sports and in the health and fitness industry. However, to date, there appears to be a lack of literature that discusses how to manage vegan diets for athletic purposes. This article attempted to review literature in order to provide recommendations for how to construct a vegan diet for athletes and exercisers. While little data could be found in the sports nutrition literature specifically, it was revealed elsewhere that veganism creates challenges that need to be accounted for when designing a nutritious diet. This included the sufficiency of energy and protein; the adequacy of vitamin B12, iron, zinc, calcium, iodine and vitamin D; and the lack of the long-chain n-3 fatty acids EPA and DHA in most plant-based sources. However, via the strategic management of food and appropriate supplementation, it is the contention of this article that a nutritive vegan diet can be designed to achieve the dietary needs of most athletes satisfactorily. Further, it was suggested here that creatine and β-alanine supplementation might be of particular use to vegan athletes, owing to vegetarian diets promoting lower muscle creatine and lower muscle carnosine levels in consumers. Empirical research is needed to examine the effects of vegan diets in athletic populations however, especially if this movement grows in popularity, to ensure that the health and performance of athletic vegans is optimised in accordance with developments in sports nutrition knowledge.
An approach to automotive ECG measurement validation using a car-integrated test framework
(2012)

Development and integration of physiological sensors into automotive applications is gaining importance. Assistance systems which possess knowledge about the driver's cognitive state could increase road safety. In this paper we present a flexible framework that enables the development, evaluation and verification of sensors and algorithms for automotive applications using physiological signals under realistic driving conditions. We have integrated a custom capacitive ECG measurement system into a test car and validated its performance in real world driving tests. During first test runs, the capacitive system achieved a sensitivity of up to 95.5% and a precision rate of up to 92.6%. Our system also records synchronized vehicle dynamics. We discuss the road test measurements which suggest that the driving situation highly impacts the quality of ECG signal. Therefore, information on driving dynamics could be used to improve the precision rate of future capacitive ECG measurement.
Spatial delineation of soil erosion vulnerability in the Lake Tana Basin , Ethiopia
(2009)

The main objective of this study was to identify the most vulnerable areas to soil erosion in the Lake Tana Basin, Blue Nile, Ethiopia using the Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, and a Geographic Information System based decision support system that uses multi-criteria evaluation (MCE). The SWAT model was used to estimate the sediment yield within each sub-basin and identify the most sediment contributing areas in the basin. Using the MCE analysis, an attempt was made to combine a set of factors (land use, soil, slope and river layers) to make a decision according to the stated objective. On the basis of simulated SWAT, sediment yields greater than 30 tons/ha for each of the sub-basin area, 18Ð4% of the watershed was determined to be high erosion potential area. The MCE results indicated that 12–30Ð5% of the watershed is high erosion potential area. Both approaches show comparable watershed area with high soil erosion susceptibility. The output of this research can aid policy and decision makers in determining the soil erosion ‘hot spots’ and the relevant soil and water conservation measures. Copyright  2009 John Wiley & Sons, Ltd.
The development of the SGI-16: a shortened sensory gating deficit and distractibility questionnaire for adults with ADHD.
(2017)

The Sensory Gating Inventory (SGI) is a questionnaire composed of 36 items designed to investigate abnormal perception related to the inability to control sensitivity to sensory stimuli frequently reported in adult with ADHD. This questionnaire can be considered too lengthy to be taken by people with ADHD, and a shortened version is needed. One hundred and sixty-three adults with ADHD responded to the SGI-36. An item reduction process took into account both the results of statistical analyses and the expertise of a steering committee. Construct validity, reliability, and external validity were tested for a short version (16 items). The structure of the SGI-16 was confirmed by principal components factor analysis. Cronbach's alpha coefficients ranged from 0.78 to 0.89. The SGI-16 dimension scores were highly correlated with their respective SGI-36 dimension scores. The SGI-16 seems to be both appropriate and useful for use in clinical practice to investigate perceptual abnormalities in adults with ADHD.
Vehicular Ad Hoc Networks (VANETs): Challenges and Perspectives
(2006)

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). These networks have no fixed infrastructure and instead rely on the vehicles themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, VANETs exhibit characteristics that are dramatically different from many generic MANETs. This article provides a comprehensive study of challenges in these networks, which we concentrate on the problems and proposed solutions. Then we outline current state of the research and future perspectives. With this article, readers can have a more thorough understanding of vehicle ad hoc networking and the research trends in this area
Design of 5.9 ghz dsrc-based vehicular safety communication
(2006)

The automotive industry is moving aggressively in the direction of advanced active safety. Dedicated short-range communication (DSRC) is a key enabling technology for the next generation of communication-based safety applications. One aspect of vehicular safety communication is the routine broadcast of messages among all equipped vehicles. Therefore, channel congestion control and broadcast performance improvement are of particular concern and need to be addressed in the overall protocol design. Furthermore, the explicit multichannel nature of DSRC necessitates a concurrent multichannel operational scheme for safety and non-safety applications. This article provides an overview of DSRC based vehicular safety communications and proposes a coherent set of protocols to address these requirements
Group signatures with verifier-local revocation
(2004)

Group signatures have recently become important for enabling privacy-preserving attestation in projects such as Microsoft's ngscb effort (formerly Palladium). Revocation is critical to the security of such systems. We construct a short group signature scheme that supports Verifier-Local Revocation (VLR). In this model, revocation messages are only sent to signature verifiers (as opposed to both signers and verifiers). Consequently there is no need to contact individual signers when some user is revoked. This model is appealing for systems providing attestation capabilities. Our signatures are as short as standard RSA signatures with comparable security. Security of our group signature (in the random oracle model) is based on the Strong Diffie-Hellman assumption and the Decision Linear assumption in bilinear groups. We give a precise model for VLR group signatures and discuss its implications.
A Survey of Inter-Vehicle Communication ∗
(2004)

As a component of the intelligent transportation system (ITS) and one of the concrete applications of mobile ad hoc networks, inter-vehicle communication (IVC) has attracted research attention from both the academia and industry of, notably, US, EU, and Japan. The most important feature of IVC is its ability to extend the horizon of drivers and on-board devices (e.g., radar or sensors) and, thus, to improve road traffic safety and efficiency. This paper surveys IVC with respect to key enabling technologies ranging from physical radio frequency to group communication primitives and security issues. The mobility models used to evaluate the feasibility of these technologies are also briefly described. We focus on the discussion of various MAC protocols that seem to be indispensable components in the network protocol stack of IVC. By analyzing the application requirements and the protocols built upon the MAC layer to meet these requirements, we also advocate our perspective that ad hoc routing protocols and group communication primitives migrated from wired networks might not be an efficient way to support the envisioned applications, and that new coordination algorithms directly based on MAC could be designed for this purpose.
Detecting Satire in the News with Machine Learning
(2018)

We built models with Logistic Regression and linear Support Vector Machines on a large dataset consisting of regular news articles and news from satirical websites, and showed that such linear classifiers on a corpus with about 60,000 articles can perform with a precision of 98.7% and a recall of 95.2% on a random test set of the news. On the other hand, when testing the classifier on “publication sources” which are completely unknown during training, only an accuracy of 88.2% and an F1-score of 76.3% are achieved. As another result, we showed that the same algorithm can distinguish between news written by the news agency itself and paid articles from customers. Here the results had an accuracy of 99%.
Dual Grid Array Antennas in a Thin-Profile Package for Flip-Chip Interconnection to Highly Integrated 60-GHz Radios
(2011)

We examine the current development of highly integrated 60-GHz radios with an interest in antenna-circuit interfaces. We design and analyze grid array antennas with special attention to the differential feeding and the patterned ground plane. More importantly, we integrate two grid array antennas in a package; propose the way of assembling it to the system printed circuit board; and demonstrate a total solution of low cost and thin profile to highly integrated 60-GHz radios. We show that the package in low temperature cofired ceramic (LTCC) technology measures only 13×13×0.575 mm3 ; can carry a 60-GHz radio die of current and future sizes with flip-chip bonding; and achieves good antenna performance in the 60-GHz band with maximum gain of 13.5 and 14.5 dBi for the single-ended and differential antennas, respectively.
NLOS Identification in Range-Based Source Localization: Statistical Approach
(2018)

Least squares estimation is a widely-used technique for range-based source localization, which obtains the most probable position of mobile station. These methods cannot provide desirable accuracy in the case with a non line of sight (NLOS) path between mobile station and base stations. To circumvent this drawback, many algorithms have been proposed to identify and mitigate this error; however, they have a large run-time overhead. On the other hand, new positioning systems utilize a large set of base stations, and a practical algorithm should be fast enough to deal with them. In this paper, we propose a novel algorithm based on subspace method to identify and eliminate the NLOS error. Simulation studies show that our algorithm is faster and more accurate compared with other conventional methods, especially in the large-scale cases.
Visual Place Recognition: A Survey
(2016)

Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines-particularly recognition in computer vision and animal navigation in neuroscience-have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition-the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.
Concepts Not Alone: Exploring Pairwise Relationships for Zero-Shot Video Activity Recognition
(2016)

Vast quantities of videos are now being captured at astonishing rates, but the majority of these are not labelled. To cope with such data, we consider the task of content-based activity recognition in videos without any manually labelled examples, also known as zero-shot video recognition. To achieve this, videos are represented in terms of detected visual concepts, which are then scored as relevant or irrelevant according to their similarity with a given textual query. In this paper, we propose a more robust approach for scoring concepts in order to alleviate many of the brittleness and low precision problems of previous work. Not only do we jointly consider semantic relatedness, visual reliability, and discriminative power. To handle noise and non-linearities in the ranking scores of the selected concepts, we propose a novel pairwise order matrix approach for score aggregation. Extensive experiments on the large-scale TRECVID Multimedia Event Detection data show the superiority of our approach.
Strategic human resource management ( SHRM ) practices and its effect on financial performance : evidence from some selected scheduled private commercial banks in Bangladesh
(2013)

Formulation and execution of Strategic Human Resource Management (SHRM) practices and its effect on perceived financial performance of any organization is a burning issue in the globalized competitive business era. This study aims to find out the relationship between SHRM and financial performance to ensure the sustainability and competitive advantage of the selected scheduled private commercial banks in Bangladesh. The research has been conducted on managers as sample of some private commercial banks during the period of January to November 2012 to collect the primary data. To evaluate the financial performance researchers have used annual reports of 2011 and 2012, journals, web sites etc. as secondary source. Survey research findings indicate that strategic integration and development of HRM were practiced to a full extent in the sampled firms. From the cross sectional analysis, the financial performance indicators show that the capital adequacy was mostly at satisfactory level compared with eh industry average. The quality of assets loan varies from bank to bank but most of them are performing at the desired level. Management efficiency was out-performing the standard in most of the cases. The profitability indicators ratio was also better than the average of private commercial banks. The result presented in this study suggests practicing intensive SHRM so that improved financial performance can be asserted to sustain in the competitive environment.
Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers
(2018)

Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.
When and why do retrieval attempts enhance subsequent encoding?
(2012)

Unsuccessful retrieval attempts can enhance subsequent encoding and learning. In three experiments, subjects either attempted to retrieve word pairs prior to studying them (e.g., attempting to recall tide-? before studying tide-beach) or did not attempt retrieval and retention of the studied targets was assessed on a subsequent cued recall test. Experiment 1 showed that attempting retrieval enhanced subsequent encoding and recall relative to not attempting retrieval when the word pairs were semantically related, but not when the pairs were unrelated. In Experiment 2, studying a different word pair prior to the correct pair (e.g., studying tide-wave prior to tide-beach) did not produce the same effect as attempting retrieval prior to studying. Constraining retrieval to a particular candidate word prior to study (e.g., recalling tide-wa__ before studying tide-beach) produced a negative effect on subsequent recall. Experiment 3 showed that attempting retrieval did not enhance encoding when a brief delay occurred between the retrieval attempt and the subsequent study trial. The results support the idea that a search set of candidates related to the retrieval cue is activated during retrieval and that this retrieval-specific activation can enhance subsequent encoding of those candidates.
Nonparametric variational inference
(2012)

Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation. The locations of these kernels and their bandwidth are treated as variational parameters and optimized to improve an approximate lower bound on the marginal likelihood of the data. Unlike most other variational approximations, using multiple kernels allows the approximation to capture multiple modes of the posterior. We demonstrate the efficacy of the nonparametric approximation with a hierarchical logistic regression model and a nonlinear matrix factorization model. We obtain predictive performance as good as or better than more specialized variational methods and MCMC approximations. The method is easy to apply to graphical models for which standard variational methods are difficult to derive.
Movie trailer quality evaluation using real-time human electroencephalogram
(2017)

The total US box office revenue exceeds ten billion dollars a year. Inevitably, new product forecasting and diagnosis have high financially stakes. The motion picture industry has a huge incentive to perform early prediction of movie success or failure. In this paper, we present a study that evaluate viewer responses to short movie trailers using a cap instrumented with Electroencephalogram (EEG) sensors. The method can be used to evaluate prerelease movies regarding how engaging they are to viewers. The primary advantage of our approach is that responses of a viewer can be recorded without any manual inputs from the viewer. The data collected, if analyzed properly, can reveal more accurate information regarding the viewer's emotion states while watching the movie than post-viewing surveys. This approach also enables the delivery of personalized entertainment through the brain interface.
A fast procedure for computing the distance between complex objects in three-dimensional space
(1988)

An efficient and reliable algorithm for computing the Euclidean distance between a pair of convex sets in Rm is described. Extensive numerical experience with a broad family of polytopes in R 3 shows that the computational cost is approximately linear in the total number of vertices specifying the two polytopes. The algorithm has special features which makes its application in a variety of robotics problems attractive. These are discussed and an example of collision
Depression and Psychosocial Risk Factors among Community-Dwelling Older Adults in Singapore.
(2015)

Depression is the most common mental and emotional disorder that emerges in the late stages of life. It is closely associated with poor health, disability, mortality, and suicide. The study examines the risk factors of depression in late life, especially the psychosocial factors, among a sample comprising 162 community-dwelling Singaporean adults aged 65 years and above. An interview-based structured survey was conducted in multiple senior activity centers located in different parts of Singapore. Results from the hierarchical regression analysis show that 32.9% of the variance in geriatric depression can be explained by the three psychosocial factors, among which loneliness, perceived social support, and the emotional regulation component of resilience are significantly associated with depression in older adults. Large-scale studies should be conducted to confirm the findings of the present study, and to further examine the predictive effects of these psychosocial factors on depression among older adults.
The least mean fourth (LMF) adaptive algorithm and its family
(1984)

New steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth, etc., sense. During adaptation, the weights undergo exponential relaxation toward their optimal solutions. T ime constants have been derived, and surprisingly they turn out to be proportional to the time constants that would have been obtained if the steepest descent least mean square (LMS) algorithm of Widrow and Hoff had been used. The new gradient algorithms are insignificantly more complicated to program and to compute than the LMS algorithm. Their general form is W J+l = w, t 2plqK-lx,, where W, is the present weight vector, W, + 1 is the next weight vector, r, is the present error, X, is the present input vector, u is a constant controlling stability and rate of convergence, and 2 K is the exponent of the error being minimized. Conditions have been derived for weight-vector convergence of the mean and of the variance for the new gradient algorithms. The behavior of the least mean fourth (LMF) algorithm is of special interest. In comparing this algorithm to the LMS algorithm, when both are set to have exactly the same time constants for the weight relaxation process, the LMF algorithm, under some circumstances, will have a substantially lower weight noise than the LMS algorithm. It is possible, therefore, that a min imum mean fourth error algorithm can do a better job of least squares estimation than a mean square error algorithm. This intriguing concept has implications for all forms of adaptive algorithms, whether they are based on steepest descent or otherwise.